ethanolivertroy/HackIDLE-NIST-Coder-GGUF

HackIDLE-NIST-Coder (GGUF)

A specialized cybersecurity LLM fine-tuned on 568 NIST publications, optimized for Ollama and llama.cpp.

Model Details

Base Model: Qwen2.5-Coder-7B-Instruct Fine-tuning: LoRA (11.5M parameters, 0.151% of base) Training Data: 568 NIST cybersecurity documents (523,706 examples) Context Length: 32,768 tokens License: Apache 2.0

Quantization Variants

File	Size	Use Case	Perplexity
hackidle-nist-coder-f16.gguf	14GB	Reference/source	Baseline
hackidle-nist-coder-q8_0.gguf	7.5GB	Highest quality	~0.1% loss
hackidle-nist-coder-q5_k_m.gguf	5.1GB	High quality	~0.5% loss
hackidle-nist-coder-q4_k_m.gguf	4.4GB	Recommended	~1% loss

Usage

With Ollama

Download and run:

ollama run ethanolivertroy/hackidle-nist-coder

Or create from this repo:

# Download GGUF
wget https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF/resolve/main/hackidle-nist-coder-q4_k_m.gguf

# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./hackidle-nist-coder-q4_k_m.gguf

SYSTEM """You are HackIDLE-NIST-Coder, a cybersecurity expert with deep knowledge of NIST standards, frameworks, and best practices."""

PARAMETER temperature 0.7
PARAMETER num_ctx 32768
EOF

# Create model
ollama create hackidle-nist-coder -f Modelfile

With llama.cpp

# Download GGUF
wget https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF/resolve/main/hackidle-nist-coder-q4_k_m.gguf

# Run inference
./llama-cli -m hackidle-nist-coder-q4_k_m.gguf \
    -p "What is Zero Trust Architecture according to NIST?" \
    -n 200 \
    --temp 0.7

With LM Studio

1. Search for "hackidle-nist-coder" in LM Studio
2. Download Q4_K_M variant
3. Start chatting!

Or use the MLX version for native Apple Silicon support.

Expertise Areas

• NIST Cybersecurity Framework (CSF)
• Risk Management Framework (RMF)
• SP 800 series security controls (AC, AU, CA, CM, CP, IA, IR, MA, MP, PE, PL, PS, RA, SA, SC, SI, SR)
• FIPS cryptographic standards
• Zero Trust Architecture (SP 800-207)
• Cloud security (SP 800-210, SP 800-144)
• Supply chain risk management (SP 800-161)
• Privacy Framework

Example Queries

"What is Zero Trust Architecture according to NIST SP 800-207?"
"Explain control AC-1 from NIST SP 800-53."
"What are the core components of the NIST Cybersecurity Framework?"
"How does NIST recommend implementing secure cloud architecture?"
"What is the Risk Management Framework process?"

Training Details

Dataset: ethanolivertroy/nist-cybersecurity-training

• 523,706 training examples
• 568 source documents
• Smart chunking with sentence boundaries
• 5 extraction strategies: sections, controls, definitions, tables, semantic chunks

Fine-tuning:

• Method: LoRA with MLX (Apple Silicon)
• Training time: 3.5 hours on M4 Max
• Iterations: 1000
• Validation loss improvement: 45%
• Base model: Qwen2.5-Coder-7B-Instruct-4bit

Performance

Ollama (M4 Max, Q4_K_M):

• Inference: 80-100 tokens/sec
• Memory: ~6GB
• Prompt processing: 50-100 tokens/sec

llama.cpp (M4 Max, Q4_K_M):

• Inference: 70-90 tokens/sec
• Memory: ~5GB

Related Models

• MLX Format: ethanolivertroy/HackIDLE-NIST-Coder-MLX-4bit
• LM Studio: ethanolivertroy/hackidle-nist-coder
• Ollama Library: ethanolivertroy/hackidle-nist-coder (coming soon)

Citation

If you use this model in your research or applications, please cite:

@software{hackidle_nist_coder,
  author = {Ethan Oliver Troy},
  title = {HackIDLE-NIST-Coder: A Fine-Tuned LLM for NIST Cybersecurity Standards},
  year = {2025},
  url = {https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF}
}

License

This model is released under the Apache 2.0 license. NIST publications are in the public domain.

Acknowledgments

• NIST for publishing comprehensive cybersecurity guidance
• Qwen Team for the exceptional Qwen2.5-Coder base model
• llama.cpp team for GGUF format and quantization
• Ollama for making local LLM deployment accessible